Devices known as brain-machine interfaces could someday be used routinely to help paralysed patients and amputees control prosthetic limbs with just their thoughts.
University of Florida (UF) researchers have taken the concept a step further, devising a way for computerised devices not only to translate brain signals into movement but also to evolve with the brain as it learns.
Instead of simply interpreting brain signals and routing them to a robotic hand or leg, this type of brain-machine interface would adapt to a person’s behaviour over time and use the knowledge to help complete a task more efficiently, say UF College of Medicine and College of Engineering researchers who developed a model system and tested it in rats.
Until now, brain-machine interfaces have been designed as one-way conversations between the brain and a computer, with the brain doing all the talking and the computer following commands. But the system that UF engineers have created allows the computer to have a say in a conversation, too.
Scientists at UF and other institutions have been studying and refining brain-machine interfaces for years, developing and testing numerous variations of the technology with the goal of creating implantable, computer-chip-sized devices capable of controlling limbs or treating diseases.
The devices are programmed with complex algorithms that interpret thoughts. But the algorithms used in current brain-machine interfaces don’t adapt to change. Until now, that is.
To create the new type of brain-machine interface, Dr Justin Sanchez, a UF assistant professor of pediatric neurology, and his colleagues developed a system based on setting goals and giving rewards.
Fitted with tiny electrodes in their brains to capture signals for the computer to unravel, three rats were taught to move a robotic arm toward a target with just their thoughts. Each time they succeeded, the rats were rewarded with a drop of water.
The computer’s goal, on the other hand, was to earn as many points as possible, Sanchez said. The closer a rat moved the arm to the target, the more points the computer received, giving it an incentive to determine which brain signals led to the most rewards, making the process more efficient for the rat.
The researchers conducted several tests with the rats, requiring them to hit targets that were farther and farther away. Despite this increasing difficulty, the rats completed the tasks more efficiently over time and did so at a significantly higher rate than if they had just aimed correctly by chance, Sanchez added.
‘We think this is how we can make these systems evolve over time,’ he said.
Dr Dawn Taylor, an assistant professor of biomedical engineering at Case Western Reserve University, said the results of the study added a new dimension to brain-machine interface research. That the Florida researchers were able to train rats to use the robotic arm and then obtain significant results from animals lacking the mental prowess of primates or humans was also impressive, she added.